No Arabic abstract
Productivity levels and growth are extremely heterogeneous among firms. A vast literature has developed to explain the origins of productivity shocks, their dispersion, evolution and their relationship to the business cycle. We examine in detail the distribution of labor productivity levels and growth, and observe that they exhibit heavy tails. We propose to model these distributions using the four parameter L{e}vy stable distribution, a natural candidate deriving from the generalised Central Limit Theorem. We show that it is a better fit than several standard alternatives, and is remarkably consistent over time, countries and sectors. In all samples considered, the tail parameter is such that the theoretical variance of the distribution is infinite, so that the sample standard deviation increases with sample size. We find a consistent positive skewness, a markedly different behaviour between the left and right tails, and a positive relationship between productivity and size. The distributional approach allows us to test different measures of dispersion and find that productivity dispersion has slightly decreased over the past decade.
What causes countercyclicality of industry--level productivity dispersion in the U.S.? Empirically, we construct an index of negative profit shocks and show that both productivity dispersion and R&D intensity dispersion enlarge at the onset of the shock and gradually dissipate. Theoretically, we build a duopolistic technology--ladder model in which heterogeneous R&D costs determine firms post--shock optimal behaviors and equilibrium technology gap. Quantitatively, we calibrate a parameterized model, simulate firms post--shock responses and predict that productivity dispersion is due to the low--cost firm increasing R&D efforts and the high--cost firm doing the opposite. We provide two empirical tests for this mechanism.
We model sectoral production by cascading binary compounding processes. The sequence of processes is discovered in a self-similar hierarchical structure stylized in the economy-wide networks of production. Nested substitution elasticities and Hicks-neutral productivity growth are measured such that the general equilibrium feedbacks between all sectoral unit cost functions replicate the transformation of networks observed as a set of two temporally distant input-output coefficient matrices. We examine this system of unit cost functions to determine how idiosyncratic sectoral productivity shocks propagate into aggregate macroeconomic fluctuations in light of potential network transformation. Additionally, we study how sectoral productivity increments propagate into the dynamic general equilibrium, thereby allowing network transformation and ultimately producing social benefits.
In the Paris agreement of 2015, it was decided to reduce the CO2 emissions of the energy sector to zero by 2050 and to restrict the global mean temperature increase to 1.5 degree Celcius above the pre-industrial level. Such commitments are possible only with practically CO2-free power generation based on variable renewable technologies. Historically, the main point of criticism regarding renewable power is the variability driven by weather dependence. Power-to-X systems, which convert excess power to other stores of energy for later use, can play an important role in offsetting the variability of renewable power production. In order to do so, however, these systems have to be scheduled properly to ensure they are being powered by low-carbon technologies. In this paper, we introduce a graphical approach for scheduling power-to-X plants in the day-ahead market by minimizing carbon emissions and electricity costs. This graphical approach is simple to implement and intuitively explain to stakeholders. In a simulation study using historical prices and CO2 intensity for four different countries, we find that the price and CO2 intensity tends to decrease with increasing scheduling horizon. The effect diminishes when requiring an increasing amount of full load hours per year. Additionally, investigating the trade-off between optimizing for price or CO2 intensity shows that it is indeed a trade-off: it is not possible to obtain the lowest price and CO2 intensity at the same time.
Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that anthropogenic climate change has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 9 years of productivity growth. The effect is substantially more severe (a reduction of ~30-33%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.
The choice of appropriate measures of deprivation, identification and aggregation of poverty has been a challenge for many years. The works of Sen, Atkinson and others have been the cornerstone for most of the literature on poverty measuring. Recent contributions have focused in what we now know as multidimensional poverty measuring. Current aggregation and identification measures for multidimensional poverty make the implicit assumption that dimensions are independent of each other, thus ignoring the natural dependence between them. In this article a variant of the usual method of deprivation measuring is presented. It allows the existence of the forementioned connections by drawing from geometric and networking notions. This new methodology relies on previous identification and aggregation methods, but with small modifications to prevent arbitrary manipulations. It is also proved that this measure still complies with the axiomatic framework of its predecessor. Moreover, the general form of latter can be considered a particular case of this new measure, although this identification is not unique.